Introducing a new method for calculating the spatial and temporal distribution of pollutants in rivers

Amiri, Siamak; Mazaheri, Mehdi; Gilan, N. Bavandpouri

doi:10.1007/s13762-020-03096-y

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…The Advection-Diffusion equation has been widely employed to study the transport of heat, mass, and water resources in 20 many areas of science and engineering, such as physics, atmospheric sciences, environmental sciences, and hydrology (e.g., Amiri et al 2021;Davydova et al 2017;Jinno et al 1993;Kawamura et al 1997;Kumar et al 2010;Perez Guerrero et al 2009;Ryu et al 2020;Shilsar et al 2023;van Genuchten 1981). For instance, pollution issues in the air and rivers have been studied using transport modeling based on the Advection-Diffusion equation (e.g., Amiri et al 2021;Shilsar et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Application of Advection-Diffusion Equation for Nonlinearly Evolving Precipitation Field

2024

Preprint

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Abstract. Analytic solutions for the Advection-Diffusion equation have been explored in diverse scientific and engineering domains, aiming to understand transport phenomena, including heat and mass diffusion, along with the movement of water resources. Precipitation, a vital component of water resources, presents a modeling challenge due to the complex interplay between advection-diffusion effects and source terms. This study aims to improve the modeling of nonlinearly evolving precipitation fields by specifically addressing advection-diffusion equations with time-varying source terms. Utilizing analytic solutions derived through the integral transform technique, we modeled the time-varying source term and investigated the correlation between advection-diffusion and source term effects. While the growth of the field is mainly influenced by the amplitude, size, and timescale of the source term, it can be modulated by advection and diffusion effects. When the timescale of source injection is significantly shorter than the dynamic scale of the system, advection and diffusion effects become independent of the field growth. Conversely, when the timescale of source term injection is sufficiently long, the system evolution primarily depends on advection and diffusion effects. In turbulent regimes with strong diffusion and weak advection effects, a quasi-equilibrium state between growth and decay can be established by regulating the decay caused by advection. However, in regimes where advection effects are crucial, the decay process predominates over the growth process.

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Section: Introductionmentioning

confidence: 99%

Application of Advection-Diffusion Equation for Nonlinearly Evolving Precipitation Field

2024

Preprint

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“…The heat conduction equation, often known as the "heat equation", is a partial differential equation (PDE) used to explain the conduction of heat through a solid. Innovative analytical approaches [15] exist for spatially homogeneous systems, and mathematicians create and evaluate most numerical techniques for homogeneous cases. However, some analytical solutions for steady-state and transient situations are also available for the onedimensional inhomogeneous system, for example, multilayer systems [16].…”

Section: Introductionmentioning

confidence: 99%

“…Weather conditions also change over time. These imply that most heat conduction issues are multi-dimensional and transient [15]. As a result, we need to use numerical computer simulation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Performance of New and Traditional Numerical Methods for Long-Term Simulations of Heat Transfer in Walls with Thermal Bridges

et al. 2023

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Several previous experiments showed that the leapfrog–hopscotch and the adapted Dufort–Frankel methods are the most efficient among the explicit and stable numerical methods to solve heat transfer problems in building walls. In this paper, we extensively measure the running times of the most successful methods and compare them to the performance of other available solvers, for example, ANSYS transient thermal analysis and the built-in routines of MATLAB, where three different mesh resolutions are used. We show that the running time of our methods changes linearly with mesh size, unlike in the case of other methods. After that, we make a long-term simulation (one full winter month) of two-dimensional space systems to test the two best versions of the methods. The real-life engineering problem we solve is the examination of thermal bridges with different shapes in buildings to increase energy efficiency.

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“…Heat conduction in a solid medium is modeled by a PDE called the "heat equation" [14]. There are rather few exact solutions for even one-dimensional inhomogeneous systems in steady-state or transient scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…However, the properties of the materials in a structure, such as density, thermal conductivity, and specific heat, can vary widely [16]. The weather changes over time, so these factors make most heat conduction problems multidimensional and transient, therefore a numerical computer simulation is required [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Thermal Loads in Buildings with Different Shapes by Neural Networks and Recent Finite Difference Methods

Askar,

Kovács,

Bolló

2023

Buildings

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This study aimed to estimate the heating load (HL) and the cooling load (CL) of a residential building using neural networks and to simulate the thermal behavior of a four-layered wall with different orientations. The neural network models were developed and tested using Multi-Layer Perceptron (MLP) and Radial Basis (RB) networks with three algorithms, namely the Levenberg-Marquardt (LM), the Scaled Conjugate Gradient (SCG), and the Radial Basis Function (RB). To generate the data, 624 models were used, including six building shapes, four orientations, five glazing areas, and five ways of distributing glazing. The LM model showed the best accuracy compared to the experimental data. The L-shape facing south with windows on the east and south sides and a 20% window area was found to be the best shape for balancing the lighting and ventilation requirements with the heating and cooling loads near the mean value. The heating and cooling loads for this shape were 22.5 kWh and 24.5 kWh, respectively. The simulation part used the LH algorithm coded in MATLAB to analyze the temperature and heat transfer across the wall layers and the effect of solar radiation. The maximum and minimum percentage differences obtained by HAP are 10.7% and 2.7%, respectively. The results showed that the insulation layer and the wall orientation were important factors for optimizing the thermal comfort of a building. This study demonstrated the effectiveness of neural networks and simulation methods for building energy analysis.

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Introducing a new method for calculating the spatial and temporal distribution of pollutants in rivers

Cited by 8 publications

References 36 publications

Application of Advection-Diffusion Equation for Nonlinearly Evolving Precipitation Field

Application of Advection-Diffusion Equation for Nonlinearly Evolving Precipitation Field

Comparison of the Performance of New and Traditional Numerical Methods for Long-Term Simulations of Heat Transfer in Walls with Thermal Bridges

Prediction and Optimization of Thermal Loads in Buildings with Different Shapes by Neural Networks and Recent Finite Difference Methods

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